Abstract

--- - This study evaluates a deep reinforcement learning agent against a state-of-the-art heuristic for resource allocation in dynamic multicore elastic optical networks (dynamic MCF-EON), focusing on various multicore fiber architectures. The distance between cores influences inter-core crosstalk (InC-XT), a key parameter. - The simulations considered the Eurocore topology, using three-core triangular fiber configurations and hexagonally arranged seven-core fibers. - The results show that DRL agents outperform heuristics by an average of 18% in blocking probability, particularly under specific inter-core distance conditions. This superiority is attributed to the adaptability of DRL agents learned during training. - The study suggests that DRL algorithms show promise in addressing resource allocation challenges in MCF-EON networks, even under strict constraints.